Transaction Verification Scoring via Corroboration
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Solution Overview
Problem
Anonymity in online transactions makes it difficult for consumers to determine the sincerity of ratings and recommendations for vendors, as they cannot verify the authenticity of the reviewers or their relationships with the vendors.
Innovation Solution
A method and system for transaction verification scoring, which involves obtaining transaction records from a distributed computing system, reconciling them to find matching corroborator records, and scoring the transaction based on the function of these corroborators to provide a verification score that ranks and recommends vendors to consumers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anonymous user names are used for online reviews, then user privacy is protected, but the authenticity and sincerity of ratings cannot be verified
Solution Approach 1:
The patent introduces a commerce network as an intermediary between consumers and vendors. This network maintains transaction records and relationship data that serve as verification evidence for reviews. The intermediary holds the truth about actual transactions and relationships, enabling authentication without exposing private user information.
Solution Approach 2:
The system implements feedback mechanisms where transaction records and relationship data are used to verify and score reviews. The verification process feeds back into the review system by assigning credibility scores to reviewers based on their transaction history and relationships, which then influences how their future reviews are weighted.
2Reliability
If detailed transaction verification is implemented, then the reliability of vendor recommendations is improved, but the complexity of the verification system increases
Solution Approach 1:
The verification system is segmented into distinct functional modules: a corroboration module that identifies and verifies transaction records, a scoring module that calculates verification scores based on multiple factors, and a recommendation module that presents verified information. This segmentation allows each module to handle specific aspects of verification independently, managing overall system complexity.
Solution Approach 2:
The system changes parameters by introducing multiple verification dimensions beyond simple transaction matching, including relationship data, reviewer history, and corroboration strength. These parameter changes enable nuanced verification scoring that improves reliability while maintaining manageable complexity through systematic parameter management.
3Measurement precision
If multiple corroborator records are analyzed, then the accuracy of transaction verification is improved, but the processing time and computational resources increase
Solution Approach 1:
The commerce network performs preliminary actions by maintaining pre-organized transaction records and relationship data in its database before verification is needed. When a review requires verification, the corroboration module can quickly query this pre-existing data structure rather than gathering information from multiple sources in real-time, significantly reducing processing time while maintaining accuracy.
Data Source
AI summary
A method for transaction verification scoring includes obtaining, from a distributed computing system of distributed computing systems distributed throughout a computing network, a transaction description describing a financial transaction with a vendor, obtaining, from the distributed computing systems, transaction records of potential corroborators, and reconciling, with the financial transaction, the transaction records to obtain at least one matching transaction record of at least one corroborator, in the potential corroborators, to the financial transaction. The method further includes scoring the transaction description based on a function of each of the at least one corroborator to the financial transaction to obtain a verification score, and presenting, on a display device, a recommendation of the vendor to a consumer based on the verification score.